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At least 235 records · Page 13Linked to original sources
PubMedDate not supplied

Artificial intelligence and Bayesian decision theory in the prediction of chemical carcinogens.

Two procedures for predicting the carcinogenicity of chemicals are described. One of these (CASE) is a self-learning artificial intelligence system that automatically recognizes activating and/or deactivating structural subunits of candidate chemicals and uses this to determine the probability that the test chemical is or is not a carcinogen. If the chemical is predicted to be carcinogen, CASE also projects its probable potency. The second procedure (CPBS) uses Bayesian decision theory to predict the potential carcinogenicity of chemicals based upon the results of batteries of short-term assays. CPBS is useful even if the test results are mixed (i.e. both positive and negative responses are obtained in different genotoxic assays). CPBS can also be used to identify highly predictive as well as cost-effective batteries of assays. For illustrative purposes the ability of CASE and CPBS to predict the carcinogenicity of a carcinogenic and a non-carcinogenic polycyclic aromatic hydrocarbon is shown. The potential for using the two methods in tandem to increase reliability and decrease cost is presented.

Animals↗
PubMed1988

CASE, the computer-automated structure evaluation method, correctly predicts the low mutagenicity for Salmonella of nitrated cyclopenta-fused polycyclic aromatic hydrocarbons.

Recently Goldring et al. [Mutation Res., 187 (1987) 67-77] reported the synthesis and purification of a series of nitro-substituted cyclopenta-fused polycyclic aromatic hydrocarbons. On the basis of expected charge distributions, these chemicals were predicted to be potent mutagens and, yet, contrary to expectation, they were found to be only weakly mutagenic for Salmonella. In their discussion, the authors suggest that application of CASE, an artificial intelligence system recently developed in these laboratories, would also not predict the low mutagenicity of this group of chemicals. In the present report, it is shown that CASE, in fact, correctly predicts the low mutagenicity of nitro-substituted cyclopenta-fused polycyclic aromatic hydrocarbons.

Mutagenicity Tests↗
PubMed2004

Metabolite profiling as an aid to metabolic engineering in plants.

The past decade has seen some impressive successes in the metabolic engineering of biotechnologically important plant pathways. However, plant metabolic engineering currently proceeds more by trial and error than by intelligent system design. A change in philosophy away from studying pathways in isolation and towards studying metabolism as a network is necessary. To support this development, improvements in technologies for metabolic analysis, a wider adoption of metabolite-profiling approaches and significant innovations in data analysis methodologies are required.

Biotechnology↗
PubMed1986

In vivo probes: problems and perspectives.

Devices constructed for potential use as invasive bioprobes incorporate a selective receiving site for molecular or ionic recognition, and a transducer which is capable of translating a perturbation of physical chemistry of the determinant-site reaction (interaction) into a usable signal. Four types are envisioned--implants for general hospital use, transient-use probes to replace classical blood tests, short-term implantable probes and the long-term variety. Performance criteria are selectivity, sensitivity, fast response, site-reversible, small, rugged, inexpensive, biocompatible, calibratible, facile use by non-expert personnel and ease of telemetry. These demands, not surprisingly, create enormous challenges to the sensor specialist. With respect to biocompatibility the sensor must not be involved in infection, clot formation or antigenic response, and, furthermore, protein adsorption, etc., which can affect the sensor response should be avoided. Calibration remains a problem of monumental proportions. Many devices drift from calibrated levels even in in vitro experiments, let alone in the implanted milieu. One solution has been to carry out on-line switching between patient blood and standard solutions. However, this type of approach leaves a lot to be desired with respect to portability. Another method which is attracting increasing attention is the chemometric or artificial intelligence system involving compensation by multi-sensor array configurations. Sensitivity and limit-of-detection have attracted little research due to the overwhelming nature of other difficulties. In the present paper we evaluate a number of these technical problems and discuss the architecture of devices that are currently available. Finally, some thoughts as to priorities for re-directing sensor research in the bioprobe area are presented.

Biocompatible Materials↗
PubMed1993

A neural network to analyze fertility data.

OBJECTIVE: To program an artificial intelligence system, a neural network, and use it to predict results of sperm penetration in bovine cervical mucus (Penetrak assay; Serono Laboratories, Norwell, MA) and zona-free hamster egg penetration from the semen analysis. DESIGN: Results of 139 Penetrak assays, 1,416 zona-free hamster egg penetration assays, and the corresponding semen analyses were retrospectively analyzed by an artificial neural network. MAIN OUTCOME MEASURES: Classification errors of the neural network were compared with those of linear and quadratic discriminant function analyses. RESULTS: Data were separated into training and test sets. For the Penetrak result, linear and quadratic discriminant function analysis correctly predicted 58% and 74% of the training set results and only 64.1% and 69.2% of the test data, respectively. The neural network correctly predicted 92% of training set results and 80% of test set results. For the zona-free hamster egg penetration assay outcome, linear and quadratic discriminant function analysis correctly classified 66.3% and 46.0% of the training set and 64.9% and 44.7% of the test set, respectively. The neural network correctly classified 75.7% of the training data and 67.8% of the test data. CONCLUSIONS: Using the semen analysis, the neural network correctly classified 67.8% of zona-free hamster egg penetration assay results and 80% of Penetrak results it had not encountered previously, suggesting that this method of data analysis may be successfully employed to predict fertility potential.

Animals↗
PubMed2003

[Techniques and current practice of urodynamics. Problems and traps].

Development of urodynamics particularly during the past decade are highlighted, as well as a number of issues to be faced in the near future: research into the etiopathogenesis of different conditions, finding more sensitive and specific diagnostic procedures to overcome the current ones, establishing more comprehensive indications for urodynamics examinations and, as a result of all the above achieving greater improvement of certain surgical procedures. Both the technique and interpretation of the different urodynamic examinations, as well as the more recent innovations, implementation issues and controversies are detailed to a highly up-to-date level. Neuromodulation and ambulatory urodynamics deserve thoughtful consideration in this paper. Finally, attention is given to the controversies and future challenges such as urodynamic research providing accurate diagnosis of lower urinary tract obstruction in women, establishing the indication of urodynamic studies in women with urinary exertional incontinence, outlining the indications of neuromodulation and ambulatory urodynamics, applicability of artificial intelligence systems, improvement of artificial sphincter materials, tissue growth for bladder enlargement and actual prevention of myelodysplasia.

Adult↗
PubMedDate not supplied

Reinforcement learning with internal expectation in the random neural networks for cascaded decisions.

The reinforcement learning scheme proposed in Halici (J. Biosystems 40 (1997) 83) for the random neural network (RNN) (Neural Computation 1 (1989) 502) is based on reward and performs well for stationary environments. However, when the environment is not stationary it suffers from getting stuck to the previously learned action and extinction is not possible. To overcome the problem, the reinforcement scheme is extended in Halici (Eur. J. Oper. Res., 126(2000) 288) by introducing a new weight update rule (E-rule) which takes into consideration the internal expectation of reinforcement. Although the E-rule is proposed for the RNN, it can be used for training learning automata or other intelligent systems based on reinforcement learning. This paper looks into the behavior of the learning scheme with internal expectation for the environments where the reinforcement is obtained after a sequence of cascaded decisions. The simulation results have shown that the RNN learns well and extinction is possible even for the cases with several decision steps and with hundreds of possible decision paths.

Computer Simulation↗
PubMed2002

Language plasticity revealed by electroencephalogram mapping.

Reasoning is the result of the computations made by intelligent systems, for instance those in the brain. It is not an abstract concept because calculations performed by computations are very concrete transactions among the different central processing unit components. Entropy measurements are proposed here to disclose the plasticity of the cerebral processing associated with language comprehension in video game playing. It is also assumed that entropy may be evaluated from the correlation coefficients obtained for the game event-related activity calculated for the different electroencephalogram derivations in the 10/20 system. The brain mapping derived from these entropy measurements clearly demonstrates the reallocation of speech functions to right brain areas when the classic left language circuits are damaged during prenatal life.

Brain Damage, Chronic↗
PubMed2025

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n&#x2009;=&#x2009;364&#xa0;733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P&#x2009;<&#x2009;.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗
PubMed1998

An RNA folding method capable of identifying pseudoknots and base triples.

MOTIVATION: Recently, we described a Maximum Weighted Matching (MWM) method for RNA structure prediction. The MWM method is capable of detecting pseudoknots and other tertiary base-pairing interactions in a computationally efficient manner (Cary and Stormo, Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology, pp. 75-80, 1995). Here we report on the results of our efforts to improve the MWM method's predictive accuracy, and show how the method can be extended to detect base interactions formerly inaccessible to automated RNA modeling techniques. RESULTS: Improved performance in MWM structure prediction was achieved in two ways. First, new ways of calculating base pair likelihoods have been developed. These allow experimental data and combined statistical and thermodynamic information to be used by the program. Second, accuracy was improved by developing techniques for filtering out spurious base pairs predicted by the MWM program. We also demonstrate here a means by which the MWM folding method may be used to detect the presence of base triples in RNAs. AVAILABILITY: http://www.cshl.org/mzhanglab/tabaska/j axpage. html CONTACT: tabaska@cshl.org

Algorithms↗
PubMed2003

Finding subtle motifs by branching from sample strings.

UNLABELLED: Many motif finding algorithms apply local search techniques to a set of seeds. For example, GibbsDNA (Lawrence et al. 1993, Science, 262, 208-214) applies Gibbs sampling to random seeds, and MEME (Bailey and Elkan, 1994, Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology (ISMB-94), 28-36) applies the EM algorithm to selected sample strings, i.e. substrings of the sample. In the case of subtle motifs, recent benchmarking efforts show that both random seeds and selected sample strings may never get close to the globally optimal motif. We propose a new approach which searches motif space by branching from sample strings, and implement this idea in both pattern-based and profile-based settings. Our PatternBranching and ProfileBranching algorithms achieve favorable results relative to other motif finding algorithms. AVAILABILITY: http://www-cse.ucsd.edu/groups/bioinformatics/software.html

Algorithms↗
PubMed1986

Computer-automated prediction of the mutagenicity of benzidine, 4,4"-diaminoterphenyl, 4-dimethylaminoazobenzene and 4-cyanodimethylaniline: comparison with the results of the Second UKEMS Collaborative Study.

There was agreement between the experimental results, obtained in the course of the Second UKEMS Collaborative Study, for the mutagenicity in Salmonella typhimurium of benzidine, 4,4"-diaminoterphenyl, 4-dimethylaminoazobenzene and 4-cyanodimethylaniline and the mutagenicity predicted by CASE (Computer Automated Structure Evaluation), a recently developed artificial intelligence system.

Aniline Compounds↗
PubMed1998

Application of Kohonen neural networks for the non-morphological distinction between glomerular and tubular renal disease.

BACKGROUND: A Kohonen topological map is an artificial intelligence system of the connectionist school (neural networks). The map learns the typical features of the subclasses in the learning set by means of a shortest Euclidean distance algorithm, after which self-adaptation of the neurons occurs. By its ability of self-organization and generalization, a Kohonen map is useful for pattern recognition, and its application in the medical field as an aid for decision making seems promising. This study describes the use of a Kohonen topological mapping system in the classification of renal diseases as being glomerular or tubular on basis of clinical characteristics and laboratory results. METHODS: Forty-one parameters from 75 patients were retrospectively retrieved and used to train four different Kohonen maps of 10 x 10 neurons. For reference diagnostic classification, we referred to the results of the light-microscopic examination. The classification of the patients by the four different Kohonen networks was compared to the classification by a rule-based system and by three nephrologists. We also developed a 'hybrid' decision system that makes a classification on basis of the opinion of the four networks and that of the rule-based system. RESULTS: The results show that a Kohonen map is capable of classifying the patients as having glomerular or tubular disease with a higher sensitivity and predictive value than the nephrologists and the rule-based system, and that the best classification was performed by the hybrid system: sensitivity and predictive value for the diagnosis 'glomerular' respectively 100 and 88% for the network with the most adequate results, 90 and 83% for the nephrologists, 90 and 95% for the rule-based system, and 95 and 96% for the hybrid system; sensitivity and predictive value for the diagnosis 'tubular' respectively 50 and 100% for the neural networks, 31 and 45% for the nephrologists, 81 and 68% for the rule-based system, and 87 and 82% for the hybrid system). CONCLUSION: We conclude that a Kohonen map is capable of classifying the patients as having glomerular or tubular disease with a high sensitivity and predictive value. The rule-based system performs worse than the neural networks. The most adequate results were obtained with the hybrid system.

Adult↗
PubMed2000

Genome annotation assessment in Drosophila melanogaster.

Computational methods for automated genome annotation are critical to our community's ability to make full use of the large volume of genomic sequence being generated and released. To explore the accuracy of these automated feature prediction tools in the genomes of higher organisms, we evaluated their performance on a large, well-characterized sequence contig from the Adh region of Drosophila melanogaster. This experiment, known as the Genome Annotation Assessment Project (GASP), was launched in May 1999. Twelve groups, applying state-of-the-art tools, contributed predictions for features including gene structure, protein homologies, promoter sites, and repeat elements. We evaluated these predictions using two standards, one based on previously unreleased high-quality full-length cDNA sequences and a second based on the set of annotations generated as part of an in-depth study of the region by a group of Drosophila experts. Although these standard sets only approximate the unknown distribution of features in this region, we believe that when taken in context the results of an evaluation based on them are meaningful. The results were presented as a tutorial at the conference on Intelligent Systems in Molecular Biology (ISMB-99) in August 1999. Over 95% of the coding nucleotides in the region were correctly identified by the majority of the gene finders, and the correct intron/exon structures were predicted for >40% of the genes. Homology-based annotation techniques recognized and associated functions with almost half of the genes in the region; the remainder were only identified by the ab initio techniques. This experiment also presents the first assessment of promoter prediction techniques for a significant number of genes in a large contiguous region. We discovered that the promoter predictors' high false-positive rates make their predictions difficult to use. Integrating gene finding and cDNA/EST alignments with promoter predictions decreases the number of false-positive classifications but discovers less than one-third of the promoters in the region. We believe that by establishing standards for evaluating genomic annotations and by assessing the performance of existing automated genome annotation tools, this experiment establishes a baseline that contributes to the value of ongoing large-scale annotation projects and should guide further research in genome informatics.

Alcohol Dehydrogenase↗
PubMed2004

Fuzzy branching temporal logic.

Intelligent systems require a systematic way to represent and handle temporal information containing uncertainty. In particular, a logical framework is needed that can represent uncertain temporal information and its relationships with logical formulae. Fuzzy linear temporal logic (FLTL), a generalization of propositional linear temporal logic (PLTL) with fuzzy temporal events and fuzzy temporal states defined on a linear time model, was previously proposed for this purpose. However, many systems are best represented by branching time models in which each state can have more than one possible future path. In this paper, fuzzy branching temporal logic (FBTL) is proposed to address this problem. FBTL adopts and generalizes concurrent tree logic (CTL*), which is a classical branching temporal logic. The temporal model of FBTL is capable of representing fuzzy temporal events and fuzzy temporal states, and the order relation among them is represented as a directed graph. The utility of FBTL is demonstrated using a fuzzy job shop scheduling problem as an example.

Journal Article↗
PubMed1996

Extracting 1/f fluctuation from the arterial blood pressure of an artificial heart.

We have studied the fluctuations of an artificial circulation for the analysis of the physiological aspects; however, the conventionally used fast Fourier transform (FFT) method cannot separate harmonic oscillations, such as respiratory and Mayer waves, from the 1/f fluctuation, which has been though to represent underlying fractal dynamics. Fractal structure was shown in the strange attractor with chaotic dynamics, which is thought to be a flexible and intelligent system. In this study, the coarse-graining spectral analyzing (CGSA) method was utilized to quantitatively evaluate the proportion of the 1/f fluctuation in the total power in the frequency domain and to analyze artificial circulation in the whole system. We implanted two pneumatically actuated ventricular assist devices as biventricular bypasses (BVBs) in chronic animal experiments using 4 healthy adult goats. To compare the natural and prosthetic circulation of each experimental animal, the BVB-type complete prosthetic circulation model with electrically induced ventricular fibrillation was adopted. All hemodynamic parameters of natural and prosthetic circulation were recorded under awake conditions and calculated with the use of a personal computer. With the use of the CGSA method, time-series data of the hemodynamics were analyzed and fractal percentages, extracting the 1/f fluctuation from a given time series, were calculated. Fractal percentages of the arterial blood pressure were 85.8 +/- 10.7% and 82.0 +/- 7.3% with natural and artificial circulation, respectively (not significant [NS]). 1/f fluctuation showed the characteristics of being fractal in a time series. The fractal structure showed robustness and error resistance in nonlinear dynamics. Therefore, our results suggest that the circulatory regulatory system of the artificial heart may have desirable characteristics such as error resistance.

Animals↗
PubMed1978

Uses of coronary heart attack registers.

By studying all coronary heart attacks presenting within defined communities it should be possible to avoid the distortions and omissions inherent in hospital-based case series. In practice the technique presents several problems. Measures of frequency and outcome are very sensitive to the diagnostic criteria used. Data of varying quality are mixed and specific attack rates can be calculated only for items for which the census provides a denominator. Patients presenting to different medical services have different outcomes, but probably less because of treatment than because the severity of the attack affects behaviour in it. Despite these problems, some such intelligence system is of value in any comprehensive strategy for coronary heart disease.

Adult↗